cyto_dl.nn.res_unit module#

class cyto_dl.nn.res_unit.ResidualUnit(spatial_dims: int, in_channels: int, out_channels: int, strides: Sequence[int] | int = 1, kernel_size: Sequence[int] | int = 3, subunits: int = 2, adn_ordering: str = 'NDA', act: tuple | str | None = 'PRELU', norm: tuple | str | None = 'INSTANCE', dropout: tuple | str | float | None = None, dropout_dim: int | None = 1, dilation: Sequence[int] | int = 1, bias: bool = True, last_conv_only: bool = False, padding: Sequence[int] | int | None = None)[source]#

Bases: Module

Residual module with multiple convolutions and a residual connection.

For example:

from monai.networks.blocks import ResidualUnit

convs = ResidualUnit(
    spatial_dims=3,
    in_channels=1,
    out_channels=1,
    adn_ordering="AN",
    act=("prelu", {"init": 0.2}),
    norm=("layer", {"normalized_shape": (10, 10, 10)}),
)
print(convs)

output:

ResidualUnit(
  (conv): Sequential(
    (unit0): Convolution(
      (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
      (adn): ADN(
        (A): PReLU(num_parameters=1)
        (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)
      )
    )
    (unit1): Convolution(
      (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
      (adn): ADN(
        (A): PReLU(num_parameters=1)
        (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)
      )
    )
  )
  (residual): Identity()
)
Args:

spatial_dims: number of spatial dimensions. in_channels: number of input channels. out_channels: number of output channels. strides: convolution stride. Defaults to 1. kernel_size: convolution kernel size. Defaults to 3. subunits: number of convolutions. Defaults to 2. adn_ordering: a string representing the ordering of activation, normalization, and dropout.

Defaults to “NDA”.

act: activation type and arguments. Defaults to PReLU. norm: feature normalization type and arguments. Defaults to instance norm. dropout: dropout ratio. Defaults to no dropout. dropout_dim: determine the dimensions of dropout. Defaults to 1.

  • When dropout_dim = 1, randomly zeroes some of the elements for each channel.

  • When dropout_dim = 2, Randomly zero out entire channels (a channel is a 2D feature map).

  • When dropout_dim = 3, Randomly zero out entire channels (a channel is a 3D feature map).

The value of dropout_dim should be no larger than the value of dimensions.

dilation: dilation rate. Defaults to 1. bias: whether to have a bias term. Defaults to True. last_conv_only: for the last subunit, whether to use the convolutional layer only.

Defaults to False.

padding: controls the amount of implicit zero-paddings on both sides for padding number of points

for each dimension. Defaults to None.

See also:

monai.networks.blocks.Convolution

forward(x: Tensor) → Tensor[source]#